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Full-Reference Image Quality Assessment with Linear Combination of Genetically Selected Quality Measures.

Oszust M - PLoS ONE (2016)

Bottom Line: The optimisation problem is solved using a genetic algorithm, which also selects suitable measures used in aggregation.Obtained multimeasures are evaluated on four largest widely used image benchmarks and compared against state-of-the-art full-reference IQA approaches.Results of comparison reveal that the proposed approach outperforms other competing measures.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Control Engineering, Rzeszow University of Technology, Rzeszow, Poland.

ABSTRACT
Information carried by an image can be distorted due to different image processing steps introduced by different electronic means of storage and communication. Therefore, development of algorithms which can automatically assess a quality of the image in a way that is consistent with human evaluation is important. In this paper, an approach to image quality assessment (IQA) is proposed in which the quality of a given image is evaluated jointly by several IQA approaches. At first, in order to obtain such joint models, an optimisation problem of IQA measures aggregation is defined, where a weighted sum of their outputs, i.e., objective scores, is used as the aggregation operator. Then, the weight of each measure is considered as a decision variable in a problem of minimisation of root mean square error between obtained objective scores and subjective scores. Subjective scores reflect ground-truth and involve evaluation of images by human observers. The optimisation problem is solved using a genetic algorithm, which also selects suitable measures used in aggregation. Obtained multimeasures are evaluated on four largest widely used image benchmarks and compared against state-of-the-art full-reference IQA approaches. Results of comparison reveal that the proposed approach outperforms other competing measures.

No MeSH data available.


Related in: MedlinePlus

Scatter plots of subjective opinion scores against scores obtained by the two best IQA measures and LCSIM3 on used datasets.Different types of distortions are represented by different colours; the set of colours is coherent within a dataset. Curves fitted with logistic functions are also shown.
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pone.0158333.g002: Scatter plots of subjective opinion scores against scores obtained by the two best IQA measures and LCSIM3 on used datasets.Different types of distortions are represented by different colours; the set of colours is coherent within a dataset. Curves fitted with logistic functions are also shown.

Mentions: Fig 2 presents the scatter plots for LCSIM3 and the two best performing IQA models for each benchmark. It can be seen that compared models for databases other than TID2013 yielded less accurate quality predictions for large DMOS values and small MOS values (i.e., in presence of severe distortions) than LCSIM3. Fig 3, in turn, contains absolute values of the difference between subjective scores and objective scores for the five best IQA measures after nonlinear fitting (Eq (3)). Here, the values were obtained for 50 images from the most popular LIVE dataset. The figure shows how scores obtained by IQA measures differ from the expected scores; smaller values are considered better. It can be seen that the introduced fusion measure, LCSIM3, returned scores which are visibly closer to subjective scores obtained in tests with human subjects. This is also confirmed by RMSE values reported for this dataset.


Full-Reference Image Quality Assessment with Linear Combination of Genetically Selected Quality Measures.

Oszust M - PLoS ONE (2016)

Scatter plots of subjective opinion scores against scores obtained by the two best IQA measures and LCSIM3 on used datasets.Different types of distortions are represented by different colours; the set of colours is coherent within a dataset. Curves fitted with logistic functions are also shown.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4920377&req=5

pone.0158333.g002: Scatter plots of subjective opinion scores against scores obtained by the two best IQA measures and LCSIM3 on used datasets.Different types of distortions are represented by different colours; the set of colours is coherent within a dataset. Curves fitted with logistic functions are also shown.
Mentions: Fig 2 presents the scatter plots for LCSIM3 and the two best performing IQA models for each benchmark. It can be seen that compared models for databases other than TID2013 yielded less accurate quality predictions for large DMOS values and small MOS values (i.e., in presence of severe distortions) than LCSIM3. Fig 3, in turn, contains absolute values of the difference between subjective scores and objective scores for the five best IQA measures after nonlinear fitting (Eq (3)). Here, the values were obtained for 50 images from the most popular LIVE dataset. The figure shows how scores obtained by IQA measures differ from the expected scores; smaller values are considered better. It can be seen that the introduced fusion measure, LCSIM3, returned scores which are visibly closer to subjective scores obtained in tests with human subjects. This is also confirmed by RMSE values reported for this dataset.

Bottom Line: The optimisation problem is solved using a genetic algorithm, which also selects suitable measures used in aggregation.Obtained multimeasures are evaluated on four largest widely used image benchmarks and compared against state-of-the-art full-reference IQA approaches.Results of comparison reveal that the proposed approach outperforms other competing measures.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer and Control Engineering, Rzeszow University of Technology, Rzeszow, Poland.

ABSTRACT
Information carried by an image can be distorted due to different image processing steps introduced by different electronic means of storage and communication. Therefore, development of algorithms which can automatically assess a quality of the image in a way that is consistent with human evaluation is important. In this paper, an approach to image quality assessment (IQA) is proposed in which the quality of a given image is evaluated jointly by several IQA approaches. At first, in order to obtain such joint models, an optimisation problem of IQA measures aggregation is defined, where a weighted sum of their outputs, i.e., objective scores, is used as the aggregation operator. Then, the weight of each measure is considered as a decision variable in a problem of minimisation of root mean square error between obtained objective scores and subjective scores. Subjective scores reflect ground-truth and involve evaluation of images by human observers. The optimisation problem is solved using a genetic algorithm, which also selects suitable measures used in aggregation. Obtained multimeasures are evaluated on four largest widely used image benchmarks and compared against state-of-the-art full-reference IQA approaches. Results of comparison reveal that the proposed approach outperforms other competing measures.

No MeSH data available.


Related in: MedlinePlus